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Publication Years
1718
3173
420
26
Category
1979
472
306
175
146
77
50
3
1
Toolboxes
706
411
398
332
302
280
208
177
129
102
96
89
88
73
70
66
64
59
59
36
31
25
4
3
Drug-Resistant tuberculosis
E.G. Brown Jr, D.S. Dooley, K. Smith
Curry International Tuberculosis Center, State of California, California Health & Human Service Agency, Department of Public Health
(2016)
C1
A survival guide for clinicans.
3rd edition.
Health care workers
M. Smelyanskaya, J. Duncan (The Focus Group Consulting); C. Daniels, et al.
Stop TB Partnership; UNOPS; END TB
(2016)
C1
Key populations brief.
Региональный проект по борьбе с туберкулезом в Восточной Европе и Центральной Азии (ТБ-REP)Среднесрочное обновление
BMC Health Services Research BMC series – open, inclusive and trusted201818:251; https://doi.org/10.1186/s12913-018-3072-3
This revised Emergency Appeal will support 400,000 of the most vulnerable people in these areas for six months, and will also undertake preparedness and prevention work in Equateur’s four neighbouring Provinces.
This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent
...
class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
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